{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f834efac8>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8350b240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.random.randn(30).cumsum(), 'ko--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.random.randn(30).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f83482e10>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f834da4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data, 'k--', label='Default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f833fa320>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f834da4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(5,5,'Hello world!')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83203c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2)\n",
    "axes[0].plot(data)\n",
    "axes[0].text(5, 5, 'Hello world!', family='monospace', fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x7f8308e320>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circ = plt.Circle((0.7, 0.2), 0.15)\n",
    "axes[1].add_patch(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7ad36668>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1449cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(10, 4).cumsum(0), \n",
    "                  columns=['A', 'B', 'C', 'D'], \n",
    "                  index=np.arange(0, 100, 10))\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7ac90ac8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7adb3a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1)\n",
    "data = pd.Series(np.random.randn(16), index=list('abcdefghijklmnop'))\n",
    "data.plot.bar(ax=axes[0], color='k', alpha=0.7)\n",
    "data.plot.barh(ax=axes[1], color='k', alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.rand(6, 4), index=['one', 'two', 'three', 'four', 'five', 'six'], columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Genus</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0.535550</td>\n",
       "      <td>0.240796</td>\n",
       "      <td>0.474435</td>\n",
       "      <td>0.705760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.615898</td>\n",
       "      <td>0.215646</td>\n",
       "      <td>0.834613</td>\n",
       "      <td>0.284651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0.446078</td>\n",
       "      <td>0.888203</td>\n",
       "      <td>0.910968</td>\n",
       "      <td>0.923536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>0.283805</td>\n",
       "      <td>0.818028</td>\n",
       "      <td>0.202542</td>\n",
       "      <td>0.891960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>0.810436</td>\n",
       "      <td>0.745234</td>\n",
       "      <td>0.290375</td>\n",
       "      <td>0.894540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>0.027407</td>\n",
       "      <td>0.673417</td>\n",
       "      <td>0.428094</td>\n",
       "      <td>0.594154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Genus         A         B         C         D\n",
       "one    0.535550  0.240796  0.474435  0.705760\n",
       "two    0.615898  0.215646  0.834613  0.284651\n",
       "three  0.446078  0.888203  0.910968  0.923536\n",
       "four   0.283805  0.818028  0.202542  0.891960\n",
       "five   0.810436  0.745234  0.290375  0.894540\n",
       "six    0.027407  0.673417  0.428094  0.594154"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7ad4f780>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f831174a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7aafd780>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ad49860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.barh(stacked=True, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7aa79ac8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ab14470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直方图和密度图\n",
    "comp1 = np.random.normal(0, 1, size=200)\n",
    "comp2 = np.random.normal(10, 2, size=200)\n",
    "values = pd.Series(np.concatenate([comp1, comp2]))\n",
    "sns.distplot(values, bins=100, color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
